Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes XP Burgos-Artizzu, D Coronado-Gutiérrez, B Valenzuela-Alcaraz, ... Scientific Reports 10 (1), 10200, 2020 | 114 | 2020 |
Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity E Bonet‐Carne, M Palacio, T Cobo, A Perez‐Moreno, M Lopez, ... Ultrasound in Obstetrics & Gynecology 45 (4), 427-433, 2015 | 91 | 2015 |
VERDICT MRI for prostate cancer: intracellular volume fraction versus apparent diffusion coefficient EW Johnston, E Bonet-Carne, U Ferizi, B Yvernault, H Pye, D Patel, ... Radiology 291 (2), 391-397, 2019 | 68 | 2019 |
Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study M Palacio, E Bonet-Carne, T Cobo, A Perez-Moreno, J Sabrià, J Richter, ... American journal of obstetrics and gynecology 217 (2), 196. e1-196. e14, 2017 | 68 | 2017 |
Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term M Sanz-Cortés, F Figueras, E Bonet-Carne, N Padilla, V Tenorio, ... Fetal Diagnosis and Therapy 33 (2), 122-129, 2013 | 64 | 2013 |
Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results L Ning, E Bonet-Carne, F Grussu, F Sepehrband, E Kaden, J Veraart, ... Neuroimage 221, 117128, 2020 | 58 | 2020 |
INNOVATE: A prospective cohort study combining serum and urinary biomarkers with novel diffusion-weighted magnetic resonance imaging for the prediction and characterization of … E Johnston, H Pye, E Bonet-Carne, E Panagiotaki, D Patel, M Galazi, ... BMC cancer 16, 1-11, 2016 | 54 | 2016 |
Performance of an automatic quantitative ultrasound analysis of the fetal lung to predict fetal lung maturity M Palacio, T Cobo, M Martínez-Terrón, GA Rattá, E Bonet-Carné, ... American journal of obstetrics and gynecology 207 (6), 504. e1-504. e5, 2012 | 47 | 2012 |
Feasibility and reproducibility of fetal lung texture analysis by automatic quantitative ultrasound analysis and correlation with gestational age T Cobo, E Bonet-Carne, M Martínez-Terrón, A Perez-Moreno, N Elías, ... Fetal diagnosis and therapy 31 (4), 230-236, 2012 | 44 | 2012 |
Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior M Sanz-Cortes, GA Ratta, F Figueras, E Bonet-Carne, N Padilla, A Arranz, ... PloS one 8 (7), e69595, 2013 | 34 | 2013 |
Transmission of SARS-CoV-2 infection among children in summer schools applying stringent control measures in Barcelona, Spain I Jordan, MF de Sevilla, V Fumado, Q Bassat, E Bonet-Carne, C Fortuny, ... Clinical infectious diseases: an official publication of the Infectious …, 2021 | 30* | 2021 |
VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization E Bonet‐Carne, E Johnston, A Daducci, JG Jacobs, A Freeman, ... NMR in Biomedicine 32 (1), e4019, 2019 | 29 | 2019 |
Generative adversarial networks to improve fetal brain fine-grained plane classification A Montero, E Bonet-Carne, XP Burgos-Artizzu Sensors 21 (23), 7975, 2021 | 27 | 2021 |
Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model V Tenorio, E Bonet-Carne, F Botet, F Marques, I Amat-Roldan, E Gratacos Journal of ultrasound in medicine 30 (10), 1365-1377, 2011 | 27 | 2011 |
Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age XP Burgos-Artizzu, D Coronado-Gutiérrez, B Valenzuela-Alcaraz, ... American Journal of Obstetrics & Gynecology MFM 3 (6), 100462, 2021 | 25 | 2021 |
Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age N Baños, A Perez-Moreno, F Migliorelli, L Triginer, T Cobo, ... Fetal diagnosis and therapy, 2016 | 23 | 2016 |
Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge M Pizzolato, M Palombo, E Bonet-Carne, CMW Tax, F Grussu, A Ianus, ... Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 …, 2020 | 22 | 2020 |
Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results L Ning, E Bonet-Carne, F Grussu, F Sepehrband, E Kaden, J Veraart, ... Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain …, 2019 | 20 | 2019 |
Multiplex antibody analysis of IgM, IgA and IgG to SARS-CoV-2 in saliva and serum from infected children and their close contacts C Dobaño, S Alonso, M Vidal, A Jiménez, R Rubio, R Santano, D Barrios, ... Frontiers in immunology 13, 751705, 2022 | 17 | 2022 |
How did the COVID-19 lockdown affect children and adolescent's well-being: Spanish parents, children, and adolescents respond S Ajanovic, J Garrido-Aguirre, B Baro, N Balanza, R Varo, ... Frontiers in public health 9, 746052, 2021 | 17 | 2021 |